Check computer science papers: citations, claims and retractions

FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

Huang, Y., Juefei-Xu, F., Wang, R., Guo, Q., Ma, L., Xie, X., Li, J., Miao, W., Liu, Y., & Pu, G. (2020) · Proceedings of the 28th ACM International Conference on Multimedia

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 71

Redundancy-Aware Topic Modeling for Patient Record Notes

Cohen, R., Aviram, I., Elhadad, M., & Elhadad, N. (2014) · PLoS ONE

Machine Learning in HealthcareTopic Modeling

Cited by 71

A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

Mans Larsson, M., Stenborg, E., Hammarstrand, L., Pollefeys, M., Sattler, T., & Kahl, F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 71

Understanding user switch of information seeking: From search engines to generative AI

Zhou, T., & Li, S. (2026) · Journal of Librarianship and Information Science

Information Retrieval and Search BehaviorTechnology Adoption and User Behaviour

Cited by 70

CarePre: An Intelligent Clinical Decision Assistance System

Jin, Z., Cui, S., Guo, S., Gotz, D., Sun, J., & Cao, N. (2020) · ACM Transactions on Computing for Healthcare

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 70

When Social Networks Meet D2D Communications: A Survey

Nitti, M., Stelea, G. A., Popescu, V., & Fadda, M. (2019) · Sensors

IoT and Edge/Fog ComputingAdvanced MIMO Systems Optimization

Cited by 70

Learning to Manipulate Articulated Objects in Unstructured Environments Using a Grounded Relational Representation

Katz, D., Pyuro, Y., & Brock, O. (2008) · Robotics: Science and Systems IV

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 70

OLMo: Accelerating the Science of Language Models

Groeneveld, D., Beltagy, I., Walsh, E., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 70

Whose AI Dream? In search of the aspiration in data annotation.

Wang, D., Prabhat, S., & Sambasivan, N. (2022) · CHI Conference on Human Factors in Computing Systems

Ethics and Social Impacts of AIAI and HR Technologies

Cited by 70

SpiNNaker: A multi-core System-on-Chip for massively-parallel neural net simulation

Painkras, E., Plana, L. A., Garside, J., Temple, S., Davidson, S., Pepper, J., Clark, D., Patterson, C., & Furber, S. (2012) · Proceedings of the IEEE 2012 Custom Integrated Circuits Conference

Interconnection Networks and SystemsAdvanced Memory and Neural Computing

Cited by 70

SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents

Cheng, K., Sun, Q., Chu, Y., Xu, F., YanTao, L., Zhang, J., & Wu, Z. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 70

Open data: Accountability and transparency

Mayernik, M. S. (2017) · Big Data & Society

Research Data Management PracticesData Analysis and Archiving

Cited by 70

Cyberattacks, Cyber Threats, and Attitudes Toward Cybersecurity Policies

Snider, K. L. G., Shandler, R., Zandani, S., & Canetti, D. (2021) · Journal of Cybersecurity

Information and Cyber SecurityCybersecurity and Cyber Warfare Studies

Cited by 70

A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents

Huang, W.-C., Zhang, W., Liang, Y., Bei, Y., Chen, Y., Feng, T., Pan, X., Tan, Z., Wang, Y., Wei, T., et al. (2026) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 70

P2p and the promise of internet equality

Agre, P. E. (2003) · Communications of the ACM

Peer-to-Peer Network TechnologiesCopyright and Intellectual Property

Cited by 70

An Adaptive e-Learning System for Enhancing Learning Performance: Based on Dynamic Scaffolding Theory

Chen, Y.-S., Wu, C.-H., & Chen, T.-G. (2017) · EURASIA Journal of Mathematics, Science and Technology Education

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 70

Privilege Escalation Attack Detection and Mitigation in Cloud Using Machine Learning

Mehmood, M., Amin, R., Muslam, M. M. A., Xie, J., & Aldabbas, H. (2023) · IEEE Access

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 70

Constrained Decoding for Neural NLG from Compositional Representations in Task-Oriented Dialogue

Balakrishnan, A., Rao, J., Upasani, K., White, M., & Subba, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 70

Evaluating the Efficiency and Effectiveness of Adaptive Parsons Problems

Ericson, B. J., Foley, J. D., & Rick, J. (2018) · Proceedings of the 2018 ACM Conference on International Computing Education Research

Teaching and Learning ProgrammingIntelligent Tutoring Systems and Adaptive Learning

Cited by 70

Stuck on semantics: Processing of irrelevant object-scene inconsistencies modulates ongoing gaze behavior

Cornelissen, T. H. W., & Võ, M. L.-H. (2017) · Attention, Perception, & Psychophysics

Visual Attention and Saliency DetectionVisual perception and processing mechanisms

Cited by 70

PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change

Valmeekam, K., Marquez, M., Olmo, A., Sreedharan, S., & Kambhampati, S. (2023) · Advances in Neural Information Processing Systems 36

AI-based Problem Solving and PlanningTopic Modeling

Cited by 70

How much do language models copy from their training data? evaluating linguistic novelty in text generation using RAVEN

McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 70

Consistent sparsification for graph optimization

Huang, G., Kaess, M., & Leonard, J. J. (2013) · 2013 European Conference on Mobile Robots

Robotics and Sensor-Based LocalizationTarget Tracking and Data Fusion in Sensor Networks

Cited by 70

On the appropriateness of Platt scaling in classifier calibration

Böken, B. (2021) · Information Systems

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 70